Search results for: mixed dataset

Authors: Hangsik Shin

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The purpose of this research is to restore the feature location of under-sampled photoplethysmogram using spline interpolation and to investigate feasibility for feature shape restoration. We obtained 10 kHz-sampled photoplethysmogram and decimated it to generate under-sampled dataset. Decimated dataset has 5 kHz, 2.5 k Hz, 1 kHz, 500 Hz, 250 Hz, 25 Hz and 10 Hz sampling frequency. To investigate the restoration performance, we interpolated under-sampled signals with 10 kHz, then compared feature locations with feature locations of 10 kHz sampled photoplethysmogram. Features were upper and lower peak of photplethysmography waveform. Result showed that time differences were dramatically decreased by interpolation. Location error was lesser than 1 ms in both feature types. In 10 Hz sampled cases, location error was also deceased a lot, however, they were still over 10 ms.

Keywords: peak detection, photoplethysmography, sampling, signal reconstruction

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Authors: Basma Khelfa, Antoine Tordeux, Ibrahima Ba

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We present a descriptive analysis data of lane-changing events in multi-lane roads. The data are provided from The Highway Drone Dataset (HighD), which are microscopic trajectories in highway. This paper describes and analyses the role of the different parameters and their significance. Thanks to HighD data, we aim to find the most frequent reasons that motivate drivers to change lanes. We used the programming language R for the processing of these data. We analyze the involvement and relationship of different variables of each parameter of the ego vehicle and the four vehicles surrounding it, i.e., distance, speed difference, time gap, and acceleration. This was studied according to the class of the vehicle (car or truck), and according to the maneuver it undertook (overtaking or falling back).

Keywords: autonomous driving, physical traffic model, prediction model, statistical learning process

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Authors: Z. Sadeghi, M. R. Omidkhah, M. E. Masoomi

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The purpose of this paper is to examine gas transport behavior of mixed matrix membranes (MMMs) combined with porous particles. Main existing models are categorized in two main groups; two-phase (ideal contact) and three-phase (non-ideal contact). A new coefficient, J, was obtained to express equations for estimating effect of the particle porosity in two-phase and three-phase models. Modified models evaluates with existing models and experimental data using Matlab software. Comparison of gas permeability of proposed modified models with existing models in different MMMs shows a better prediction of gas permeability in MMMs.

Keywords: mixed matrix membrane, permeation models, porous particles, porosity

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Authors: Tamanna Jethava, Rahul Joshi

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Frequent Item sets play an essential role in many data Mining tasks that try to find interesting patterns from the database. Typically it refers to a set of items that frequently appear together in transaction dataset. There are several mining algorithm being used for frequent item set mining, yet most do not scale to the type of data we presented with today, so called “BIG DATA”. Big Data is a collection of large data sets. Our approach is to work on the frequent item set mining over the large dataset with scalable and speedy way. Big Data basically works with Map Reduce along with HDFS is used to find out frequent item sets from Big Data on large cluster. This paper focuses on using pre-processing & mining algorithm as hybrid approach for big data over Hadoop platform.

Keywords: frequent item set mining, big data, Hadoop, MapReduce

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Authors: Oluwaponmile David Alao

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Misdiagnosis has been the major problem in health sector. Heart attack has been one of diseases that have high level of misdiagnosis recorded on the part of physicians. In this paper, an intelligent system has been developed for diagnosis of heart attack in the health sector. Dataset of heart attack obtained from UCI repository has been used. This dataset is made up of thirteen attributes which are very vital in diagnosis of heart disease. The system is developed on the multilayer perceptron trained with back propagation neural network then simulated with feed forward neural network and a recognition rate of 87% was obtained which is a good result for diagnosis of heart attack in medical field.

Keywords: heart attack, artificial neural network, diagnosis, intelligent system

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Authors: Arpan Kumar Nayak, Debabrata Pradhan

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A simple, single-step and one-pot hydrothermal method was employed to synthesize WO₃-SnO₂ mixed nanostructured metal oxides at 200°C in 12h. The SnO₂ nanoparticles were found to be uniformly decorated on the WO₃ nanoplates. Though it is widely known that noble metals such as Pt, Pd doping or decoration on metal oxides improve the sensing response and sensitivity, we varied the SnO₂ concentration in the WO₃-SnO₂ mixed oxide and demonstrated their performance in ammonia, ethanol and acetone sensing. The sensing performance of WO₃-(x)SnO₂ [x = 0.27, 0.54, 1.08] mixed nanostructured oxides was found to be not only superior to that of pristine oxides but also higher/better than that of reported noble metal-based sensors. The sensing properties (selectivity, limit of detection, response and recovery times) are measured as a function of operating temperature (150-350°C). In particular, the gas selectivity is found to be highly temperature-dependent with optimum performance obtained at 200°C, 300°C and 350°C for ammonia, ethanol, and acetone, respectively. The present results on cost effective WO₃-SnO₂ sensors can find potential application in human breath analysis by noninvasive detection.

Keywords: gas sensing, mixed oxides, nanoplates, ammonia, ethanol, acetone

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Authors: Roghayyeh Motallebzadeh, Shahin Hajizadeh, Mohammad Reza Ghasemi

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Laminar mixed convection heat transfer of a nanofluid with prescribed constant heat flux on the inner wall of horizontal annular tube has been studied numerically based on two-phase mixture model in different Rayleigh numbers and Azimuth angles. Effects of applying of different volume fractions of Al2O3 nanoparticles in water as a base fluid on hydrodynamic and thermal behaviours of the fluid flow such as axial velocity, secondary flow, temperature, heat transfer coefficient and friction coefficient at the inner and outer wall region, has been investigated. Conservation equations in elliptical form has been utilized and solved in three dimensions for a steady flow. It is observed that, there is a good agreement between results in this work and previously published experimental and numerical works on mixed convection in horizontal annulus. These particles cause to increase convection heat transfer coefficient of the fluid, meanwhile there is no considerable effect on friction coefficient.

Keywords: buoyancy force, laminar mixed convection, mixture model, nano-fluid, two-phase

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Authors: Tetsuro Saeki, Yuichi Kato, Shoutarou Mizuno

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STRIM (Statistical Test Rule Induction Method) has been proposed as a method to effectively induct if-then rules from the decision table which is considered as a sample set obtained from the population of interest. Its usefulness has been confirmed by simulation experiments specifying rules in advance, and by comparison with conventional methods. However, scope for future development remains before STRIM can be applied to the analysis of real-world data sets. The first requirement is to determine the size of the dataset needed for inducting true rules, since finding statistically significant rules is the core of the method. The second is to examine the capacity of rule induction from datasets with contaminated attribute values created by missing data and noise, since real-world datasets usually contain such contaminated data. This paper examines the first problem theoretically, in connection with the rule length. The second problem is then examined in a simulation experiment, utilizing the critical size of dataset derived from the first step. The experimental results show that STRIM is highly robust in the analysis of datasets with contaminated attribute values, and hence is applicable to realworld data.

Keywords: rule induction, decision table, missing data, noise

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Authors: Mahdi Fakoor, Seyed Mohammad Navid Ghoreishi

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Investigation of fracture of wood components can prevent from catastrophic failures. Created fracture process zone (FPZ) in crack tip vicinity has important effect on failure of cracked composite materials. In this paper, a failure criterion for fracture investigation of cracked wood specimens under mixed mode I/II loading is presented. This criterion is based on maximum strain energy release rate and material nonlinearity in the vicinity of crack tip due to presence of microcracks. Verification of results with available experimental data proves the coincidence of the proposed criterion with the nature of fracture of wood. To simplify the estimation of nonlinear properties of FPZ, a damage factor is also introduced for engineering and application purposes.

Keywords: fracture criterion, mixed mode loading, damage zone, micro cracks

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Authors: Vo Ngoc Phu, Phan Thi Tuoi

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We have explored different methods of improving the accuracy of sentiment classification. The sentiment orientation of a document can be positive (+), negative (-), or neutral (0). We combine five dictionaries from [2, 3, 4, 5, 6] into the new one with 21137 entries. The new dictionary has many verbs, adverbs, phrases and idioms, that are not in five ones before. The paper shows that our proposed method based on the combination of Term-Counting method and Enhanced Contextual Valence Shifters method has improved the accuracy of sentiment classification. The combined method has accuracy 68.984% on the testing dataset, and 69.224% on the training dataset. All of these methods are implemented to classify the reviews based on our new dictionary and the Internet Movie data set.

Keywords: sentiment classification, sentiment orientation, valence shifters, contextual, valence shifters, term counting

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Authors: Rosa Figueroa, Christopher Flores

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Text categorization is the problem of assigning a new document to a set of predetermined categories, on the basis of a training set of free-text data that contains documents whose category membership is known. To train a classification model, it is necessary to extract characteristics in the form of tokens that facilitate the learning and classification process. In text categorization, the feature extraction process involves the use of word sequences also known as N-grams. In general, it is expected that documents belonging to the same category share similar features. The Smith-Waterman (SW) algorithm is a dynamic programming algorithm that performs a local sequence alignment in order to determine similar regions between two strings or protein sequences. This work explores the use of SW algorithm as an alternative to feature extraction in text categorization. The dataset used for this purpose, contains 2,610 annotated documents with the classes Obese/Non-Obese. This dataset was represented in a matrix form using the Bag of Word approach. The score selected to represent the occurrence of the tokens in each document was the term frequency-inverse document frequency (TF-IDF). In order to extract features for classification, four experiments were conducted: the first experiment used SW to extract features, the second one used unigrams (single word), the third one used bigrams (two word sequence) and the last experiment used a combination of unigrams and bigrams to extract features for classification. To test the effectiveness of the extracted feature set for the four experiments, a Support Vector Machine (SVM) classifier was tuned using 20% of the dataset. The remaining 80% of the dataset together with 5-Fold Cross Validation were used to evaluate and compare the performance of the four experiments of feature extraction. Results from the tuning process suggest that SW performs better than the N-gram based feature extraction. These results were confirmed by using the remaining 80% of the dataset, where SW performed the best (accuracy = 97.10%, weighted average F-measure = 97.07%). The second best was obtained by the combination of unigrams-bigrams (accuracy = 96.04, weighted average F-measure = 95.97) closely followed by the bigrams (accuracy = 94.56%, weighted average F-measure = 94.46%) and finally unigrams (accuracy = 92.96%, weighted average F-measure = 92.90%).

Keywords: comorbidities, machine learning, obesity, Smith-Waterman algorithm

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Authors: Mohamed Shawki Elamir, Heinrich Gotzig, Raoul Zoellner, Patrick Maeder

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In this work, a methodology is presented for identifying the optimal digitization parameters for the analog signal of ultrasonic sensors. These digitization parameters are the resolution of the analog to digital conversion and the sampling rate. This is accomplished through the derivation of characteristic curves based on Fano inequality and the calculation of the mutual information content over a given dataset. The mutual information is calculated between the examples in the dataset and the corresponding variation in the feature that needs to be estimated. The optimal parameters are identified in a manner that ensures optimal estimation performance while preventing inefficiency in using unnecessarily powerful analog to digital converters.

Keywords: analog to digital conversion, digitization, sampling rate, ultrasonic

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Authors: Thidaya Thitiapichart

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The effect of additional magnesium oxide (MgO) was investigated by using the tungsten oxide supported on silica catalyst (WOx/SiO2) physically mixed with MgO in a weight ratio 1:1. The both fresh and spent catalysts were characterized by FT-Raman spectrometer, UV-Vis spectrometer, X-Ray diffraction (XRD), and temperature programmed oxidation (TPO). The results indicated that the additional MgO could enhance the conversion of trans-2-butene due to isomerization reaction. However, adding MgO would increase the amount of coke deposit on the WOx/SiO2 catalyst. The TPO profile presents two peaks when the WOx/SiO2 catalyst was physically mixed with MgO. The further peak was suggested to be coming from the coke precursor that could be produced by isomerization reaction of the undesired product. Then, the occurred coke precursor could deposit and form coke on the acid catalyst.

Keywords: coke formation, metathesis, magnesium oxide, physically mix

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Authors: Ting-YuLiu, Yi-Feng Lin

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Porous Prussian Blue (PB) nanoparticles were successfully incorporated into chitosan (CS) membranes to form PB/CS mixed-matrix membranes (MMMs) and the as-prepared PB/CS MMMs were used to dehydration of ethanol at 25 oC in the pervaporation process. The effect of PB loading in CS matrix on pervaporation performance was investigated. The FESEM, EDS, FTIR and XRD measurements were also used for the characterization of the PB/CS MMMs. The PB/CS membranes with 30 wt% PB loading show the best performance with the permeate flux of 614 g/m2h and the separation factor of 1472. The pervaporation using the PB/CS membranes exhibits outstanding performance as compared with the previously reported CS based membranes and MMMs. The present work demonstrates good pervaporation performance of the PB/CS MMMs for the separation of 90wt% ethanol aqueous solution, moreover, it has an opportunity for dehydration of bioethanol in this system of pervaporation.

Keywords: pervaporation, chitosan, Prussian blue, mixed-matrix membrane

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Authors: Subhadip Biswas, Shivendra Maurya, Satish Chandra, Indrajit Ghosh

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Developing speed model is a challenging task particularly under mixed traffic condition where the traffic composition plays a significant role in determining vehicular speed. The present research has been conducted to model individual vehicular speed in the context of mixed traffic on an urban arterial. Traffic speed and volume data have been collected from three midblock arterial road sections in New Delhi. Using the field data, a volume based speed prediction model has been developed adopting the methodology of Artificial Neural Network (ANN). The model developed in this work is capable of estimating speed for individual vehicle category. Validation results show a great deal of agreement between the observed speeds and the predicted values by the model developed. Also, it has been observed that the ANN based model performs better compared to other existing models in terms of accuracy. Finally, the sensitivity analysis has been performed utilizing the model in order to examine the effects of traffic volume and its composition on individual speeds.

Keywords: speed model, artificial neural network, arterial, mixed traffic

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Authors: Kriuk Boris, Kriuk Fedor

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This paper presents a comparative study of fundamental similarity functions for Siamese networks in semantic textual similarity (STS) tasks. We evaluate various similarity functions using the STS Benchmark dataset, analyzing their performance and stability. Additionally, we introduce a multi-objective approach for optimal threshold selection. Our findings provide insights into the effectiveness of different similarity functions and offer a straightforward method for threshold selection optimization, contributing to the advancement of Siamese network architectures in STS applications.

Keywords: siamese networks, semantic textual similarity, similarity functions, STS benchmark dataset, threshold selection

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Authors: Ahmad FAROOQ, Xinfeng Zhang, Fahad Sabah, Raheem Sarwar

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In Traditional Chinese Medicine, tongue diagnosis is a vital inspection tool (TCM). In this study, we explore the potential of machine learning in tongue diagnosis. It begins with the cataloguing of the various classifications and characteristics of the human tongue. We infer 24 kinds of tongues from the material and coating of the tongue, and we identify 21 attributes of the tongue. The next step is to apply machine learning methods to the tongue dataset. We use the Weka machine learning platform to conduct the experiment for performance analysis. The 457 instances of the tongue dataset are used to test the performance of five different machine learning methods, including SVM, Random Forests, Decision Trees, and Naive Bayes. Based on accuracy and Area under the ROC Curve, the Support Vector Machine algorithm was shown to be the most effective for tongue diagnosis (AUC).

Keywords: medical imaging, image retrieval, machine learning, tongue

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Authors: M. Bilal Ishfaq, Adnan N. Qureshi

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COVID-19 is the most infectious disease these days, it was first reported in Wuhan, the capital city of Hubei in China then it spread rapidly throughout the whole world. Later on 11 March 2020, the World Health Organisation (WHO) declared it a pandemic. Since COVID-19 is highly contagious, it has affected approximately 219M people worldwide and caused 4.55M deaths. It has brought the importance of accurate diagnosis of respiratory diseases such as pneumonia and COVID-19 to the forefront. In this paper, we propose a hybrid approach for the automated detection of COVID-19 using medical imaging. We have presented the hybridization of manually extracted and convolutional features. Our approach combines Haralick texture features and convolutional features extracted from chest X-rays and CT scans. We also employ a minimum redundancy maximum relevance (MRMR) feature selection algorithm to reduce computational complexity and enhance classification performance. The proposed model is evaluated on four publicly available datasets, including Chest X-ray Pneumonia, COVID-19 Pneumonia, COVID-19 CTMaster, and VinBig data. The results demonstrate high accuracy and effectiveness, with 0.9925 on the Chest X-ray pneumonia dataset, 0.9895 on the COVID-19, Pneumonia and Normal Chest X-ray dataset, 0.9806 on the Covid CTMaster dataset, and 0.9398 on the VinBig dataset. We further evaluate the effectiveness of the proposed model using ROC curves, where the AUC for the best-performing model reaches 0.96. Our proposed model provides a promising tool for the early detection and accurate diagnosis of COVID-19, which can assist healthcare professionals in making informed treatment decisions and improving patient outcomes. The results of the proposed model are quite plausible and the system can be deployed in a clinical or research setting to assist in the diagnosis of COVID-19.

Keywords: COVID-19, feature engineering, artificial neural networks, radiology images

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Authors: Naren Bag, S. Bhattacharyya, Partha P. Gopmandal

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In this study, we have analyzed the transport of analytes under a two dimensional steady incompressible flow of power-law fluids through rectangular nanochannel. A mathematical model based on the Cauchy momentum-Nernst-Planck-Poisson equations is considered to study the combined effect of mixed electroosmotic (EO) and pressure driven (PD) flow. The coupled governing equations are solved numerically by finite volume method. We have studied extensively the effect of key parameters, e.g., flow behavior index, concentration of the electrolyte, surface potential, imposed pressure gradient and imposed electric field strength on the net average flow across the channel. In addition to study the effect of mixed EOF and PD on the analyte distribution across the channel, we consider a nonlinear model based on general convective-diffusion-electromigration equation. We have also presented the retention factor for various values of electrolyte concentration and flow behavior index.

Keywords: electric double layer, finite volume method, flow behavior index, mixed electroosmotic/pressure driven flow, non-Newtonian power-law fluids, numerical simulation

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Authors: Hozaifa Zaki, Ghada Soliman

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In this paper, we introduced an approach for Automated Dotted Arabic Expiration Date Extraction using Optimized Convolutional Autoencoder (ARABEX) with bidirectional LSTM. This approach is used for translating the Arabic dot-matrix expiration dates into their corresponding filled-in dates. A custom lightweight Convolutional Recurrent Neural Network (CRNN) model is then employed to extract the expiration dates. Due to the lack of available dataset images for the Arabic dot-matrix expiration date, we generated synthetic images by creating an Arabic dot-matrix True Type Font (TTF) matrix to address this limitation. Our model was trained on a realistic synthetic dataset of 3287 images, covering the period from 2019 to 2027, represented in the format of yyyy/mm/dd. We then trained our custom CRNN model using the generated synthetic images to assess the performance of our model (ARABEX) by extracting expiration dates from the translated images. Our proposed approach achieved an accuracy of 99.4% on the test dataset of 658 images, while also achieving a Structural Similarity Index (SSIM) of 0.46 for image translation on our dataset. The ARABEX approach demonstrates its ability to be applied to various downstream learning tasks, including image translation and reconstruction. Moreover, this pipeline (ARABEX+CRNN) can be seamlessly integrated into automated sorting systems to extract expiry dates and sort products accordingly during the manufacturing stage. By eliminating the need for manual entry of expiration dates, which can be time-consuming and inefficient for merchants, our approach offers significant results in terms of efficiency and accuracy for Arabic dot-matrix expiration date recognition.

Keywords: computer vision, deep learning, image processing, character recognition

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Authors: M. M. Rahman, A. Liu, A. Frostegard, J. Frostegard

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The human immune system plays an essential role in cardiovascular disease (CVD) and atherosclerosis. Our earlier studies showed that major immunocompetent cells including T cells are activated by phosphorylcholine epitope. Further, we have determined for the first time in a clinical cohort that antibodies against phosphorylcholine (anti-PC) are negatively and independently associated with the development of atherosclerosis and thus a low risk of cardiovascular diseases. It is still unknown whether activated T cells play a role in anti-PC production. Here we aim to clarify the role of T cells in anti-PC production. B cell alone, or with CD3 T, CD4 T or with CD8 T cells were cultured in polystyrene plates to examine anti-PC IgM production. In addition to mixed B cell with CD3 T cell culture, B cells with CD3 T cells were also cultured in transwell co-culture plates. Further, B cells alone and mixed B cell with CD3 T cell cultures with or without anti-HLA 2 antibody were cultured for 6 days. Anti-PC IgM was detected by ELISA in independent experiments. More than 8 fold higher levels of anti-PC IgM were detected by ELISA in mixed B cell with CD3 T cell cultures in comparison to B cells alone. After the co-culture of B and CD3 T cells in transwell plates, there were no increased antibody levels indicating that B and T cells need to interact to augment anti-PC IgM production. Furthermore, anti-PC IgM was abolished by anti-HLA 2 blocking antibody in mixed B and CD3 T cells culture. In addition, the lack of increased anti-PC IgM in mixed B with CD8 T cells culture and the increased levels of anti-PC in mixed B with CD4 T cells culture support the role of helper T cell for the anti-PC IgM production. Atherosclerosis is a major cause of cardiovascular diseases, but anti-PC IgM is a protection marker for atherosclerosis development. Understanding the mechanism involved in the anti-PC IgM regulation could play an important role in strategies to raise anti-PC IgM. Studies suggest that anti-PC is T-cell independent antibody, but our study shows the major role of T cell in anti-PC IgM production. Activation of helper T cells by immunization could be a possible mechanism for raising anti-PC levels.

Keywords: anti-PC, atherosclerosis, aardiovascular diseases, phosphorylcholine

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Authors: Mario Gianni, Federico Nardi, Federico Ferri, Filippo Cantucci, Manuel A. Ruiz Garcia, Karthik Pushparaj, Fiora Pirri

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In this paper, we describe a Mixed-Initiative Operational Model (MIOM) which directly intervenes on the state of the functionalities embedded into a robot for Urban Search&Rescue (USAR) domain applications. MIOM extends the reasoning capabilities of the vehicle, i.e. mapping, path planning, visual perception and trajectory tracking, with operator knowledge. Especially in USAR scenarios, this coupled initiative has the main advantage of enhancing the overall performance of a rescue mission. In-field experiments with rescue responders have been carried out to evaluate the effectiveness of this operational model.

Keywords: mixed-initiative planning and control, operator control interfaces for rescue robotics, situation awareness, urban search, rescue robotics

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Authors: Wael Al-Kouz

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Numerical simulations to study heat transfer and flow characteristics of mixed convection for rarefied gas in a square enclosure are utilized. Effect of the geometry in terms of the location of the inlet and exit openings are investigated. Moreover, effect of Knudsen number on the flow and heat transfer characteristics is illustrated and discussed. Results of the simulations show that there is a configuration that yields better heat transfer. This configuration is found to be the geometry in which the inlet opening is in the top left corner and the exit opening is at the bottom right corner. In addition, it is found that by increasing Knudsen number, Nusselt number will decrease.

Keywords: Knudsen number, mixed convection, rarefied gas, square enclosure

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Authors: Erin Micali, Katerina Tolstikova, Cheryl Maykel, Elizabeth Harwood

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Female ASD is seldomly studied separately from males. Further, females with ASD are disproportionately underrepresented in the research at a rate of 3:1 (male to female). As such, much of the current understanding about female rigid repetitive behaviors (RRBs) stems from research’s understanding of male RRBs. This can be detrimental to understanding female ASD because this understanding of female RRBs largely discounts female camouflaging and the possibility that females present their autistic symptoms differently. Current literature suggests that females with ASD engage in fewer RRBs than males with ASD and when females do engage in RRBs, they are likely to engage in more subtle, less overt obsessions and repetitive behaviors than males. Method: The current study utilized a mixed methods research design to identify the type and frequency of RRBs that females with ASD engaged in by using a cross-sectional design. The researcher recruited only females to be part of the present study with the criteria they be at least age six and not have co-occurring cognitive impairment. Results: The researcher collected previous testing data (Autism Diagnostic Interview-Revised (ADI-R), Child or Adolescent/Adult Sensory Profile-2, Autism/ Empathy Quotient, Yale Brown Obsessive Compulsive Checklist, Rigid Repetitive Behavior Checklist (evaluator created list), and demographic questionnaire) from 25 total participants. The participants ages ranged from six to 52. The participants were 96% Caucasion and 4% Latin American. Qualitative analysis found the current participant pool engaged in six RRB themes including repetitive behaviors, socially restrictive behaviors, repetitive speech, difficulty with transition, obsessive behaviors, and restricted interests. The current dataset engaged in socially restrictive behaviors and restrictive interests most frequently. Within the main themes 40 subthemes were isolated, defined, and analyzed. Further, preliminary quantitative analysis was run to determine if age impacted camouflaging behaviors and overall presentation of RRBs. Within this dataset this was not founded. Further qualitative data will be run to determine if this dataset engaged in more overt or subtle RRBs to confirm or rebuff previous research. The researcher intends to run SPSS analysis to determine if there was statistical difference between each RRB theme and overall presentation. Secondly, each participant will be analyzed for presentation of RRB, age, and previous diagnoses. Conclusion: The present study aimed to assist in diagnostic clarity. This was achieved by collecting data from a female only participant pool across the lifespan. Current data aided in clarity of the type of RRBs engage in. A limited sample size was a barrier in this study.

Keywords: autism spectrum disorder, camouflaging, rigid repetitive behaviors, gender disparity

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Authors: Yang Chen, Lili Fu

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Promoting urban regeneration in existing areas has been elevated to a national strategy in China. In this context, because of the multidimensional sustainable effect through the intensive use of land, mixed-use development has become an important objective for high-quality urban regeneration in the inner city. However, in the long period of time since China's reform and opening up, the "unbalanced phenomenon" of mixed-use development in China's inner cities has been very serious. On the one hand, the excessive focus on certain individual spaces has led to an increase in the level of mixed-use development in some areas, substantially ahead of others, resulting in a growing gap between different parts of the inner city; On the other hand, the excessive focus on a one-dimensional element of the spatial organization of mixed-use development, such as the enhancement of functional mix or spatial capacity, has led to a lagging phenomenon or neglect in the construction of other dimensional elements, such as pedestrian permeability, green environmental quality, social inclusion, etc. This phenomenon is particularly evident in the central area of the inner city, and it clearly runs counter to the need for sustainable development in China's new era. Therefore, a rational qualitative and quantitative analysis of the "unbalanced phenomenon" will help to identify the problem and provide a basis for the formulation of relevant optimization plans in the future. This paper builds a dynamic evaluation method of mixed-use development based on a meta-dimensional model and then uses spatial evolution analysis and spatial consistency analysis with ArcGIS software to reveal the "unbalanced phenomenon " in over the past 40 years of the central city area in Nanjing, a China’s typical city facing regeneration. This study result finds that, compared to the increase in functional mix and capacity, the dimensions of residential space mix, public service facility mix, pedestrian permeability, and greenness in Nanjing's city central area showed different degrees of lagging improvement, and the unbalanced development problems in each part of the city center are different, so the governance and planning plan for future mixed-use development needs to fully address these problems. The research methodology of this paper provides a tool for comprehensive dynamic identification of mixed-use development level’s change, and the results deepen the knowledge of the evolution of mixed-use development patterns in China’s inner cities and provide a reference basis for future regeneration practices.

Keywords: mixed-use development, unbalanced phenomenon, the meta-dimensional model, over the past 40 years of Nanjing, China

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Authors: Abdallah H. Ramini, Alwathiqbellah I. Ibrahim, Mohammad I. Younis

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We investigate experimentally and theoretically the dynamics of a capacitive resonator under mixed frequency excitation of two AC harmonic signals. The resonator is composed of a proof mass suspended by two cantilever beams. Experimental measurements are conducted using a laser Doppler Vibrometer to reveal the interesting dynamics of the system when subjected to two-source excitation. A nonlinear single-degree-of-freedom model is used for the theoretical investigation. The results reveal combination resonances of additive and subtractive type, which are shown to be promising to increase the bandwidth of the resonator near primary resonance frequency. Our results also demonstrate the ability to shift the combination resonances to much lower or much higher frequency ranges. We also demonstrate the dynamic pull-in instability under mixed frequency excitation.

Keywords: electrostatically actuated resonator, multi-frequency excitation, nonlinear dynamics, AC harmonic signals

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Authors: P. Shrivastava, A. Shukla, K. Verma, S. Rungta

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Parkinson's disease is a chronic neurological disorder that directly affects human gait. It leads to slowness of movement, causes muscle rigidity and tremors. Gait serve as a primary outcome measure for studies aiming at early recognition of disease. Using gait techniques, this paper implements efficient binary bat algorithm for an early detection of Parkinson's disease by selecting optimal features required for classification of affected patients from others. The data of 166 people, both fit and affected is collected and optimal feature selection is done using PSO and Bat algorithm. The reduced dataset is then classified using neural network. The experiments indicate that binary bat algorithm outperforms traditional PSO and genetic algorithm and gives a fairly good recognition rate even with the reduced dataset.

Keywords: parkinson, gait, feature selection, bat algorithm

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Authors: Sanjib Kr Pal, S. Bhattacharyya

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Mixed convection of Cu-water nanofluid in an enclosure with thick wavy bottom wall has been investigated numerically. A co-ordinate transformation method is used to transform the computational domain into an orthogonal co-ordinate system. The governing equations in the computational domain are solved through a pressure correction based iterative algorithm. The fluid flow and heat transfer characteristics are analyzed for a wide range of Richardson number (0.1 ≤ Ri ≤ 5), nanoparticle volume concentration (0.0 ≤ ϕ ≤ 0.2), amplitude (0.0 ≤ α ≤ 0.1) of the wavy thick- bottom wall and the wave number (ω) at a fixed Reynolds number. Obtained results showed that heat transfer rate increases remarkably by adding the nanoparticles. Heat transfer rate is dependent on the wavy wall amplitude and wave number and decreases with increasing Richardson number for fixed amplitude and wave number. The Bejan number and the entropy generation are determined to analyze the thermodynamic optimization of the mixed convection.

Keywords: conjugate heat transfer, mixed convection, nano fluid, wall waviness

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Authors: Hung-Yi Chen, Shih-Jye Wu

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The existing literature provides little discussion on why a public monopolist gives up its market dominant position and allows private firms entering the market. We argue that the privatization of a public monopolist under a vertically related market may induce the entry of private firms. We develop a model of a mixed oligopoly with vertically related markets to explain the change in the market from a public monopolist to a mixed oligopoly and examine issues on privatizing the downstream public enterprise both in the short run and long run in the vertically related markets. We first show that the welfare-maximizing public monopoly firm is suboptimal in the vertically related markets. This is due to the fact that the privatization will reduce the input price charged by the upstream foreign monopolist. Further, the privatization will induce the entry of private firms since input price will decrease after privatization. Third, we demonstrate that the complete privatizing the public firm becomes a possible solution if the entry cost of private firm is low. Finally, we indicate that the public firm should partially privatize if the free-entry of private firms is allowed. JEL classification: F12, F14, L32, L33

Keywords: free entry, mixed oligopoly, public monopoly, the initiation of privatization, vertically related markets, mixed oligopoly

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Authors: Qisheng Hu, Jianglei Han, Yue Yang, My Hoa Ha

Abstract:

When a company is considering purchasing software for commercial use, contract risk assessment is critical to identify risks to mitigate the potential adverse business impact, e.g., security, financial and regulatory risks. Contract risk assessment requires reviewers with specialized knowledge and time to evaluate the legal documents manually. Specifically, validating contracts for a software vendor requires the following steps: manual screening, interpreting legal documents, and extracting risk-prone segments. To automate the process, we proposed a framework to assist legal contract document risk identification, leveraging pre-trained deep learning models and natural language processing techniques. Given a set of pre-defined risk evaluation problems, our framework utilizes the pre-trained transformer-based models for question-answering to identify risk-prone sections in a contract. Furthermore, the question-answering model encodes the concatenated question-contract text and predicts the start and end position for clause extraction. Due to the limited labelled dataset for training, we leveraged transfer learning by fine-tuning the models with the CUAD dataset to enhance the model. On a dataset comprising 287 contract documents and 2000 labelled samples, our best model achieved an F1 score of 0.687.

Keywords: contract risk assessment, NLP, transfer learning, question answering

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